> Markdown version of [/jobs/ext/1927762-applied-data-science-engineer](https://www.wearedevelopers.com/jobs/ext/1927762-applied-data-science-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Applied Data Science Engineer

- **Company:** siemens energy
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Computer Programming, DevOps, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Azure Machine Learning, Software Engineering, Software Factory, Feature Engineering, Pytorch, Large Language Models, Model Validation, Backend, Pandas, Scikit Learn, Kubernetes, Information Technology, Data Analytics, Machine Learning Operations
- **Published:** August 5, 2026
- **Apply:** https://www.buscojobs.com.es/applied-data-science-engineer-en-madrid-ID-365566957

## About the Role

Master's degree in Data Science, Computer Science, Applied Mathematics, or a related technical field.
5+ years of experience applying classical data science and statistical modeling to real-world problems.
Strong programming skills in Python and hands-on experience with libraries such as pandas, scikit-learn, NumPy.
Familiarity with ML/AI frameworks (e.g., PyTorch, TensorFlow) and experience with experimentation and model evaluation.
Understanding of MLOps and experience with tools like MLflow, Airflow, or Kubeflow is a plus.
Bonus: Knowledge of LLMs, GenAI architectures, or deployment of models to edge environments.

## Description

Overview A Snapshot of Your DayAs anApplied Data Science Engineerwithin theScalable Coreteam, you will work at the intersection of data science, software engineering, and AI/ML/GenAI innovation.You will design and implement intelligent, production-ready solutions for energy management by applying statistical models and building reusable, scalable components for our company-wide AI/ML/GenAI platform.Your contributions will directly enable energy management solutions across cloud, on-premises, and edge environments - supporting teams throughout Siemens Energy in delivering smarter, more efficient products.You will be surrounded by technical experts in scalable systems, DevOps, MLOps, and AI, all collaborating to shape the future of energy through software.How You'll Make An ImpactApply statistical and machine learning techniques to energy and industrial data to create predictive and prescriptive models.Design reusable data science components for preprocessing, feature engineering, and statistical evaluation.Collaborate with MLOps engineers to operationalize models in robust, scalable ML pipelines.Contribute to experimentation workflows, model explainability, uncertainty quantification, and fairness evaluation.Support automatic model selection, tuning, and lifecycle monitoring across environments (cloud, on-prem, edge).Translate business and engineering requirements into data-driven solutions in collaboration with cross-functional teams.What You BringMaster's degree in Data Science, Computer Science, Applied Mathematics, or a related technical field.5+ years of experience applying classical data science and statistical modeling to real-world problems.Strong programming skills in Python and hands-on experience with libraries such as pandas, scikit-learn, NumPy.Familiarity with ML/AI frameworks (e.g., PyTorch, TensorFlow) and experience with experimentation and model evaluation.Understanding of MLOps and experience with tools like MLflow, Airflow, or Kubeflow is a plus.Bonus: Knowledge of LLMs, GenAI architectures, or deployment of models to edge environments.About The TeamTheScalable Coreteam drives the foundational technologies and developer platforms that empower scalable and intelligent energy management solutions across Siemens Energy.We build reusable backend services, AI/ML platforms, and lifecycle tools to support cloud, on-prem, and edge deployments.Our mission is to enable teams to deliver intelligent solutions faster and with higher quality - through automation, developer experience, and scalable software foundations.Whether it's through a streamlined Software Factory Console, advanced model lifecycle capabilities, or self-service deployment engines, we ensure that engineering teams can focus on solving energy challenges - not reinventing the wheel.Who is Siemens Energy?At Siemens Energy, we are more than just an energy technology company.With ~****** dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably.The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation.Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible.We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.Find out how you can make a difference at Siemens Energy: Our Commitment to DiversityLucky for us, we are not all the same.Through diversity we generate power.We run on inclusion and our combined creative energy is fueled by over 130 nationalities.Siemens Energy celebrates character - no matter what ethnic background, gender, age, religion, identity, or disability.We energize society, all of society, and we do not discriminate based on our differences.Rewards and BenefitsCompetitive compensation packageWork-life balance: Flexible working timeFlexible mobile working policy (hybrid)Local benefits such as meal allowance, flexible plan and much moreSelf-driven development framework with insights and resources to develop and grow on technical and soft skills.Continuous learningInternational and cross Business UnitsWe value equal opportunities and welcome applications from people with disabilities.#J-*****-Ljbffr


## Related Videos

- [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast)
- [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform)
- [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data)
- [How to implement convenient Python bindings to C++](https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Top-Paying Tech Jobs (with Salaries)](https://www.wearedevelopers.com/magazine/372-top-paying-tech-jobs-with-salaries)